CN111930809A

CN111930809A - Data processing method, device and equipment

Info

Publication number: CN111930809A
Application number: CN202010978072.9A
Authority: CN
Inventors: 韩喆
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2020-09-17
Filing date: 2020-09-17
Publication date: 2020-11-13
Also published as: EP3971806A1; EP3971806B1; US20210326357A1; US11436252B2

Abstract

The embodiment of the specification provides a data processing method, a data processing device and data processing equipment, wherein the method comprises the following steps: the method comprises the steps that a block chain node receives a data processing request sent by a first user, a first intelligent contract deployed in a block chain is called, and a characteristic value of first original data to be processed included in the data processing request is extracted based on the first intelligent contract; matching the historical data stored in the block chain according to the extracted characteristic value to determine whether target historical data with the similarity meeting a preset condition with the first original data exists in the historical data; and carrying out corresponding processing according to the matching result information of the matching processing.

Description

Data processing method, device and equipment

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a data processing method, device and apparatus.

Background

In recent years, copyright protection has been receiving increasing attention. In order to avoid infringement, the works of the users are generally subjected to a judging process. The current re-judgment processing is mainly performed by a hash value matching mode based on a centralized database. However, the centralized database has a risk of being attacked, so that the reliability of the re-judging processing result is not high; and because a punctuation mark is modified or a pixel value of an image is modified, the calculated hash value will be different, so that the work can be avoided by slightly modifying, and the accuracy of the re-judgment processing result is lower.

Disclosure of Invention

One or more embodiments of the present specification provide a data processing method applied to a blockchain node. The method includes receiving a data processing request sent by a first user. Wherein the data processing request comprises first raw data to be processed. Calling a first intelligent contract deployed in a blockchain, and extracting a characteristic value of the first original data based on the first intelligent contract. And matching the historical data stored in the block chain according to the characteristic value. And determining whether target historical data with the similarity meeting preset conditions with the first original data exists in the historical data. And carrying out corresponding processing according to the matching result information of the matching processing.

One or more embodiments of the present specification provide a data processing apparatus applied to a blockchain node. The device comprises a receiving module for receiving a data processing request sent by a first user. Wherein the data processing request comprises first raw data to be processed. The device further comprises an extraction module, wherein the extraction module is used for calling a first intelligent contract deployed in the block chain and extracting a characteristic value of the first original data based on the first intelligent contract. The device further comprises a matching module, and the matching module is used for matching historical data stored in the block chain according to the characteristic value so as to determine whether target historical data with the similarity meeting a preset condition with the first original data exists in the historical data. The device also comprises a processing module which carries out corresponding processing according to the matching result information of the matching processing.

One or more embodiments of the present specification provide a data processing apparatus. The apparatus includes a processor. The apparatus also comprises a memory arranged to store computer executable instructions. The computer-executable instructions, when executed, cause the processor to receive a data processing request sent by a first user. Wherein the data processing request comprises first raw data to be processed. Calling a first intelligent contract deployed in a blockchain, and extracting a characteristic value of the first original data based on the first intelligent contract. And matching the historical data stored in the block chain according to the characteristic value. And determining whether target historical data with the similarity meeting preset conditions with the first original data exists in the historical data. And carrying out corresponding processing according to the matching result information of the matching processing.

One or more embodiments of the present specification provide a storage medium. The storage medium is used to store computer-executable instructions. The computer-executable instructions, when executed by the processor, receive a data processing request sent by a first user. Wherein the data processing request comprises first raw data to be processed. Calling a first intelligent contract deployed in a blockchain, and extracting a characteristic value of the first original data based on the first intelligent contract. And matching the historical data stored in the block chain according to the characteristic value. And determining whether target historical data with the similarity meeting preset conditions with the first original data exists in the historical data. And carrying out corresponding processing according to the matching result information of the matching processing.

Drawings

In order to more clearly illustrate one or more embodiments or technical solutions in the prior art in the present specification, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive exercise;

fig. 1 is a schematic diagram of a first scenario of a data processing method according to one or more embodiments of the present disclosure;

fig. 2 is a schematic diagram of a second scenario of a data processing method according to one or more embodiments of the present disclosure;

fig. 3 is a first flowchart of a data processing method according to one or more embodiments of the present disclosure;

fig. 4 is a second flowchart of a data processing method according to one or more embodiments of the present disclosure;

fig. 5 is a third flowchart of a data processing method according to one or more embodiments of the present disclosure;

fig. 6 is a fourth flowchart of a data processing method according to one or more embodiments of the present disclosure;

fig. 7 is a fifth flowchart of a data processing method according to one or more embodiments of the present disclosure;

fig. 8 is a sixth flowchart of a data processing method according to one or more embodiments of the present disclosure;

fig. 9 is a seventh flowchart of a data processing method according to one or more embodiments of the present disclosure;

fig. 10 is an eighth flowchart of a data processing method according to one or more embodiments of the present disclosure;

fig. 11 is a ninth flowchart illustrating a data processing method according to one or more embodiments of the disclosure;

FIG. 12 is a block diagram of a data processing apparatus according to one or more embodiments of the present disclosure;

fig. 13 is a schematic structural diagram of a data processing apparatus according to one or more embodiments of the present disclosure.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.

Fig. 1 is a schematic view of an application scenario of a data processing method according to one or more embodiments of the present specification, as shown in fig. 1, the scenario includes: a first terminal device of a first user and at least one blockchain node (only one shown in fig. 1) of an access blockchain; the first terminal device may be a mobile phone, a tablet computer, a desktop computer, a portable notebook computer, or the like (only the mobile phone is shown in fig. 1); data such as work data uploaded by a user is stored in the block chain, wherein the work data includes data such as written works, movie and television works, photographic works, computer software and the like.

Specifically, a first user operates a first terminal device of the first user to send a data processing request to a block link node; the method comprises the steps that a first intelligent contract deployed in a block chain is called by a block chain node, first original data to be processed are obtained from a data processing request based on the first intelligent contract, and a characteristic value of the first original data is extracted; matching the historical data stored in the block chain according to the extracted characteristic value to determine whether target historical data with the similarity meeting the preset condition with the first original data exists in the historical data stored in the block chain, performing corresponding processing according to matching result information of the matching processing, and sending processing result information to the first terminal equipment; and the first terminal equipment displays the received processing result information. The data processing request includes a data storage request, a retrieval request and the like, and the first original data includes original written works, movie and television works, photographic works, computer software and the like.

Further, when the data processing request is a data storage request, as shown in fig. 2, the scenario may further include: a second terminal device of a second user corresponding to the target historical data; the second terminal device may be a mobile phone, a tablet computer, a desktop computer, a portable notebook computer, or the like (only the mobile phone is shown in fig. 2). When the block link point determines that the target historical data exists, sending first prompt information of similar data to the first terminal device and sending second prompt information of the similar data to the second terminal device respectively; the first terminal device displays the received first prompt message, and the second terminal device displays the received second prompt message.

Therefore, by storing the work data of the user into the block chain and performing matching processing based on the historical data stored in the block chain when receiving the data processing request, the uncollapsibility of the data stored in the block chain is ensured, and the accuracy of the matching result is further ensured; and based on the characteristics of the block chain, the problems that the matching result is not credible and the like caused by being attacked are avoided. Meanwhile, by extracting the characteristic values and performing matching processing based on the characteristic values, the user is difficult to avoid even simply modifying the works, so that the accuracy of the matching result is improved; moreover, the characteristic value is extracted based on the intelligent contract, so that the extraction rule of the characteristic value can be traced, upgrading optimization can be performed, the extraction performance of the characteristic value is continuously improved, and the accuracy of the matching result is continuously improved.

Based on the application scenario architecture, one or more embodiments of the present specification provide a data processing method. Fig. 3 is a flowchart illustrating a data processing method according to one or more embodiments of the present disclosure, where the method in fig. 3 can be performed by the block chain node in fig. 1, as shown in fig. 3, and the method includes the following steps:

step S102, receiving a data processing request sent by a first user; the data processing request comprises first original data to be processed;

the first original data to be processed comprises work data, such as data of written works, movie and television works, photographic works, computer software and the like. It should be noted that the first original data to be processed is not limited to work data, and all data with a criterion requirement may be within the protection scope of this document.

Step S104, calling a first intelligent contract deployed in the block chain, and extracting a characteristic value of first original data based on the first intelligent contract;

step S106, performing matching processing on historical data stored in the block chain according to the characteristic value to determine whether target historical data with the similarity meeting a preset condition with the first original data exists in the historical data;

and step S108, carrying out corresponding processing according to the matching result information of the matching processing.

In one or more embodiments of the present specification, when a data processing request is received by a blockchain node, extracting a feature value of first original data in the data processing request based on a first intelligent contract in the blockchain; and matching the historical data stored in the block chain according to the extracted characteristic value to determine whether the historical data has target historical data with the similarity meeting the preset condition with the first original data, and performing corresponding processing according to the matching result information of the matching processing. Therefore, by storing the work data of the user into the block chain and performing matching processing based on the historical data stored in the block chain when receiving the data processing request, the uncollapsibility of the data stored in the block chain is ensured, and the accuracy of the matching result is further ensured; and based on the characteristics of the block chain, the problems that the matching result is not credible and the like caused by being attacked are avoided. Meanwhile, by extracting the characteristic values and performing matching processing based on the characteristic values, the user is difficult to avoid even simply modifying the works, so that the accuracy of the matching result is improved; moreover, the characteristic value is extracted based on the intelligent contract, so that the extraction rule of the characteristic value can be traced, upgrading optimization can be performed, the extraction performance of the characteristic value is continuously improved, and the accuracy of the matching result is continuously improved.

In order to quickly determine a target extraction rule when a data processing request is received, in one or more embodiments of the present specification, an association relationship between data type information and the extraction rule is preset. Specifically, as shown in fig. 4, the step S104 may include the following steps S104-2 to S104-6:

step S104-2, calling a first intelligent contract deployed in the block chain, and determining data type information of first original data based on the first intelligent contract;

optionally, a type identification mode of the data type is set in the first intelligent contract in advance; accordingly, step S104-2 includes: and identifying the first original data according to a preset type identification mode based on the first intelligent contract to obtain the data type information of the first original data. The type identification mode may be set in practical application as needed, and is not limited in this specification. Or, the first user may specify the data type information of the first original data when sending the data processing request, and accordingly, step S104-2 includes: data type information of the first original data is obtained from the data processing request based on the first intelligent contract.

Step S104-4, obtaining a target extraction rule in the incidence relation between the data type information and the extraction rule included in the first intelligent contract according to the determined data type information;

the extraction rule of the characteristic value can adopt the existing extraction rule of any characteristic value, and can be set in practical application according to the requirement. As an example, when the data type information is a text type, the target extraction rule is TF-IDF (Term Frequency-Inverse Document Frequency); when the data type is an image type, the target extraction rule is Scale-Invariant Feature Transform (SIFT), speedup Robust Features (Speeded Up Robust Features), or the like; when the data type is a video type, each frame image or key frame image of the video data can be acquired, and a feature value and the like are extracted from each acquired frame image or key frame image based on a target extraction rule corresponding to the image type.

And step S104-6, extracting the characteristic value of the first original data according to the target extraction rule.

Since the extraction rules of the above examples are well known to those skilled in the art, the detailed process of extracting feature values based on the target extraction rule will not be described in detail in this specification.

By setting the incidence relation between the data type information and the extraction rule in the first intelligent contract, the target extraction rule can be rapidly determined based on the incidence relation when a data processing request is received, and the extraction rate of the characteristic value is further improved.

Further, in order to ensure that each block link point can perform feature value extraction on the same type of first original data based on the same extraction rule, in one or more embodiments of the present specification, a plurality of second intelligent contracts may be further deployed in the block chain, where each second intelligent contract includes a feature value extraction rule for data of one data type. Accordingly, as shown in fig. 5, the step S104 may include the following steps S104-8 to S104-10:

step S104-8, calling a first intelligent contract in the block chain, and determining the data type information of the first original data based on the first intelligent contract;

the implementation manner of this step is the same as that of step S104-2, and reference may be made to the foregoing description, and repeated details are not described here.

Step S104-10, selecting a target second intelligent contract matched with the determined data type information from a plurality of second intelligent contracts deployed in advance, and extracting a characteristic value of the first original data based on the target second intelligent contract; and the second intelligent contract is provided with an extraction rule of the characteristic value of the first original data of the corresponding data type.

Specifically, according to the determined data type information, obtaining associated target contract identification information in the association relationship between the preset data type information and the contract identification information; and taking the second intelligent contract corresponding to the acquired target contract identification information as the target second intelligent contract matched with the determined data type information. And calling a target second intelligent contract, and extracting the characteristic value of the first original data based on the target second intelligent contract.

Therefore, by deploying the second intelligent contract and setting the extraction rule of the characteristic value of the first original data of the corresponding data type in the second intelligent contract, different block chain link points are corresponding to the first original data of the same type, and the characteristic value is extracted through the same second intelligent contract, so that the management and the upgrade of each extraction rule are facilitated, the block chain link points are ensured to have the same characteristic value extraction rule, the uniqueness of the characteristic value extracted from the same first original data is ensured, and the credibility of the matching result is further improved.

In consideration of the fact that the data volume of historical data stored in a block chain is often large, in order to improve matching efficiency, in one or more embodiments of the present specification, a fuzzy search is first performed based on a feature value to obtain candidate historical data; and then determining target historical data from the candidate historical data based on an accurate similarity calculation mode. Specifically, as shown in fig. 6, the step S106 includes the following steps S106-2:

step S106-2, fuzzy search processing is carried out on the characteristic value of the first original data and the characteristic value of the historical data stored in the block chain, so as to determine whether target historical data with the similarity meeting preset conditions with the first original data exists in the historical data;

optionally, when the history data and the feature values thereof are saved to the blockchain, the history data, the feature values of the history data, the user information (such as user identification, name, age, and the like) of the second user corresponding to the history data, and the like are recorded in an associated manner, and the recorded information is saved in the blockchain; accordingly, step S106-2 includes: and carrying out fuzzy search processing on the characteristic value of the first original data and the characteristic value associated with the historical data stored in the block chain to obtain a target characteristic value, and determining the historical data associated with the target characteristic as candidate historical data meeting a first preset condition. Or when the historical data and the characteristic values thereof are stored in the block chain, firstly, the historical data, the user information of the second user corresponding to the historical data and the like are stored in the block chain in an associated manner, then the characteristic values of the historical data and the storage addresses of the historical data are recorded in an associated manner, and the recorded information is stored in a specified storage area in the block chain, wherein the specified storage area comprises the associated relationship between the characteristic values of the historical data and the storage addresses; accordingly, step S106-2 includes: and performing fuzzy search processing on the characteristic value of the first original data and the characteristic value in the designated storage area of the block chain to obtain a target characteristic value, and determining historical data corresponding to a storage address associated with the target characteristic value as candidate historical data meeting a first preset condition.

Corresponding to step S106-2, as shown in fig. 6, step S108 includes the following step S108-2:

and step S108-2, performing corresponding processing according to the processing result information.

Further, in order to meet the requirement of high throughput data processing, in one or more embodiments of the present specification, an index manner that does not involve a tampering problem and has a processing speed on the millisecond level is preset, and accordingly, as shown in fig. 7, step S106-2 may include the following steps S106-22 to S106-26:

step S106-22, according to a preset indexing mode, indexing the characteristic value of the first original data and the characteristic value corresponding to the historical data stored in the block chain to obtain a target characteristic value meeting a first preset condition; and determining the historical data corresponding to the target characteristic value as candidate historical data.

The target characteristic values meeting the first preset condition may be characteristic values which are ranked from high to low according to the similarity with the characteristic values of the first original data and are located at the top N; the feature values at the last N may also be sorted from low to high according to the similarity with the feature value of the first original data. N is greater than or equal to 1, and may be set as required in practical application, for example, N is 5. Further, the indexing method includes word segmentation, inverted indexing, word vector indexing, and the like. It should be noted that the indexing method is not limited to the above method, and it can also be set in practical applications as needed. Since the indexing method is well known to those skilled in the art, the detailed indexing process will not be described in detail in this specification. And performing preliminary screening based on the characteristic values according to an index mode, and quickly obtaining candidate historical data, so that target historical data is determined based on an accurate similarity calculation method.

S106-24, calculating the similarity of the first original data and each candidate historical data according to a preset similarity calculation mode;

for the first original data of different data types, different similarity calculation modes can be adopted. For example, for data in a text form, word vector similarity, similarity of deep learning language and legal book, and the like are adopted; for image data, cosine similarity, histogram similarity, machine learning and the like are adopted; for video data, similarity calculation may be performed for each frame of image data or key frame image. It should be noted that the similarity calculation method is not limited to the above method, and may be set in practical applications as needed.

And S106-6, determining whether target historical data with the similarity meeting a second preset condition with the first original data exists in the historical data according to the calculated similarity.

The target historical data meeting the second preset condition can be historical data which is ranked from high to low according to the similarity and is positioned at the top M; or the historical data at the last M in the sequence from low similarity to high similarity. Wherein, M is greater than or equal to 1, and can be set in practical application according to needs, for example, M is 3. Or, the target historical data meeting the second preset condition may be historical data with a similarity greater than a preset similarity threshold with the first original data. It is to be noted that, when the target history data meeting the second preset condition may be history data having a similarity to the first original data greater than a preset similarity threshold, the target history data may not exist.

Further, corresponding to the above steps S106-22 to S106-26, as shown in fig. 7, the step S108 may include the following steps S108-22:

and S108-22, performing corresponding processing according to the determined result information.

Therefore, fuzzy search is firstly carried out based on the characteristic values according to a preset indexing mode, and the similarity between the first original data and each historical data is not directly calculated one by one based on a more complex similarity calculation method, so that candidate historical data can be quickly obtained; then, the similarity between the first original data and each candidate historical data can be calculated based on an accurate similarity calculation method, and target historical data are obtained; therefore, on the basis of ensuring the accuracy of the target historical data, the determining rate of the target historical data is greatly improved, and the data processing requirement of high throughput can be met.

It should be noted that, for a specific implementation manner of the foregoing step S108-2 and step S108-22, reference may be made to the related description of step S108 in the following, and details are not repeated here.

In practical applications, some users may choose to directly save the work data in the blockchain after the work creation is completed in order to avoid loss or malicious tampering. Accordingly, the data processing request is a data evidence storage request. Specifically, as shown in fig. 8, step S102 may include the following step S102-2:

step S102-2, receiving a data evidence storage request sent by a first user; the data certificate storing request comprises first original data to be certified;

corresponding to step S102-2, as shown in fig. 8, step S104 may further include the following step S105:

step S105; and associating and storing the extracted characteristic value and the first original data into a block chain, and sending successful evidence storage information to the first user.

Optionally, the extracted feature value, the first original data, the user information of the first user, and the like are recorded in association, and the recorded information is saved in the block chain. Or, storing the first original data and the user information of the first user in a block chain in an associated manner, recording the storage address of the first original data in an associated manner with the characteristic value, and storing the recorded information in a specified storage area of the block chain; wherein, the appointed storage area comprises the association relation between the storage address of each historical data and the characteristic value. Therefore, when a data processing request is subsequently received, corresponding processing can be carried out based on the association relation in the designated storage area.

Further, considering that when the target history data exists, namely, the infringement risk that the first user infringes the right of the second user corresponding to the target history data exists; in order to make the first user timely aware of the infringement risk and to make the second user timely maintain the right of the second user, in one or more embodiments of the present specification, corresponding to step S102-2 and step S105, as shown in fig. 8, step S108 may include the following step S108-4:

step S108-4, if the target historical data is determined to exist according to the matching result information of the matching processing, first prompt information with similar data is sent to the first user according to the relevant information of the target historical data, and second prompt information with similar data is sent to the second user corresponding to the target historical data according to the relevant information of the first original data.

In order to ensure the privacy of the data, the related information of the target historical data, such as the characteristic value of the target historical data, the corresponding user information of the second user, and the like; when the target history data has been published in the network, the related information of the target history data may also be a link address of the target history data or the like. The related information of the first original data is, for example, a feature value of the first original data, user information of the first user, and the like. The user information includes, for example, name, age, etc.

Further, if it is determined that the target historical data does not exist according to the matching result information of the matching process, third prompt information for representing that similar data does not exist can be sent to the first user, so that the first user can determine that the first original data of the first user does not have the risk of infringement; alternatively, no information is sent to the first user.

Therefore, when the received data authentication request is received, the first original data to be authenticated and the characteristic value of the first original data are stored in the block chain, so that the non-tamper property of the first original data and the characteristic value is ensured, and an effective basis is provided for the subsequent data processing request. When the target historical data is determined to exist, the first user can modify the works of the first user in time by sending prompt information to the first user and the second user, so that infringement is avoided; and the second user can realize that the copyright of the second user is at risk of being violated, so that measures can be taken in time to guarantee the rights and interests of the second user.

When the first user receives the first prompt message, the first user can modify the first original data of the first user according to the relevant information of the target historical data included in the first prompt message to obtain second original data, and sends a data storage request to the block link point again according to the second original data. Correspondingly, as shown in fig. 9, step S108-4 may be followed by:

step S110, receiving a data evidence storing request sent by a first user; the data evidence storing request comprises second original data to be stored and obtained by updating the first original data based on the first prompt information;

step S112, performing a storage processing on the second raw data.

Specifically, in order to enable the block link point to distinguish whether the data to be stored is the data of the initial version or the updated data, in one or more embodiments of the present specification, the data storage request may further include version information of the data to be stored, data identification information, and the like, and the modified data is the same as the data identification information of the data of the previous version. Correspondingly, when the data to be stored is stored in the block chain, the data to be stored, the version information of the data to be stored, the data identification information and the like are stored in the block chain in an associated manner. Thus, in step S112, the blockchain node acquires the version information of the second original data to be stored from the data storage request, and if it is determined that the acquired version information is not the preset initial version information, determines other data in the blockchain except the data related to the second original data as historical data, and returns to step S104 to perform processing such as extracting a feature value and matching the historical data according to the feature value; for the specific processes of the extraction of the feature values, the matching of the historical data, and the like, reference may be made to the foregoing related description, and repeated details are not described here. Optionally, the version information is arranged in an ascending order, such as V1.0, V1.1, V1.2, etc. The data related to the second original data is, for example, data having the same data identification information as the second original data and having a version that is a version before the version of the second original data. As an example, the version of the second original data is V1.2, and the data identification information is 0003, then the related data of the second original data includes data with data identification information of 0003, and versions of V1.0 and V1.1. It should be noted that, when the history data stored in the blockchain has multiple versions, the last version of the data is determined as the final data of the corresponding second user.

Further, when the data processing request is a data storage request, after extracting the feature of the first original data, the block link point may execute step S105 and simultaneously perform step S106 in background and silent; therefore, the saving operation and the matching operation are executed in parallel, and the processing efficiency can be improved.

Further, when the data processing request is a data authentication request, step S105 may also be performed after step S108. After extracting the features of the first original data, the blockchain node may also perform matching processing on historical data stored in the blockchain according to the extracted features to determine whether there is target historical data in the historical data, where the similarity between the target historical data and the first original data meets a preset condition; and sending prompt information to the first user when determining that the target history data exists, and executing step S105 when receiving determination saving information sent by the first user. Specifically, as shown in fig. 10, step S108 may include the following step S108-6:

step S108-6: if the target historical data is determined to exist according to the matching result information of the matching process, sending fourth prompt information to the first user so that the first user can determine whether to store the first original data to be stored in the block chain or not; if the confirmation save information sent by the first user is received, step S105 is executed

The fourth prompting message may be the same as or different from the first prompting message. Therefore, if the target historical data is determined to exist, the third prompt message is sent to the first user before the first original data to be stored is stored in the block chain, so that the first user can determine whether the first original data to be stored is stored in the block chain, and the occurrence of an infringement event can be effectively avoided.

Further, if it is determined that there is no target historical data according to the matching result information of the matching process, step S105 is executed, and third prompt information indicating that there is no similar data may be sent to the first user, so that the first user determines that there is no risk of infringement on the first original data; alternatively, step S105 is performed.

In practical application, some users have query requirements of similar works in the process of creating the works, so that the similar works are avoided from being created. Based on this, in one or more embodiments of the present specification, the data processing request may also be a retrieval request; accordingly, as shown in fig. 11, step S102 includes the following step S102-4:

step S102-4, receiving a retrieval request sent by a first user; the retrieval request comprises first original data to be queried;

corresponding to step S102-4, as shown in fig. 11, step S108 may include the following step S108-8:

and step S108-8, if the target historical data is determined to exist according to the matching result information of the matching processing, the retrieval result information is sent to the first user according to the related information of the target historical data.

Further, if it is determined that the target history data does not exist according to the matching result information of the matching process, the search result information representing that similar data does not exist is sent to the first user.

Therefore, when the received data processing request is a retrieval request, the retrieval result information is sent to the first user based on the matching result information of the matching process, and the first user can avoid the infringement risk when creating works.

Further, the first user may further specify a target profile similar to the first original data, where the target profile may be history data stored in the blockchain or other data not stored in the blockchain. Correspondingly, the data processing request also comprises related information of a target corresponding file, when the block link point determines candidate historical data based on the data processing request, the target comparison file is also determined as the candidate historical data, and the similarity between the first original data and each candidate historical data is calculated according to a preset similarity calculation mode.

On the basis of the same technical concept, corresponding to the data processing methods described in fig. 3 to 11, one or more embodiments of the present specification further provide a data processing apparatus. Fig. 12 is a schematic block diagram of a data processing apparatus according to one or more embodiments of the present disclosure, the apparatus being configured to perform the data processing method described in fig. 3 to 11, and as shown in fig. 12, the apparatus includes:

a receiving module 201, configured to receive a data processing request sent by a first user; the data processing request comprises first original data to be processed;

the extraction module 202 is used for calling a first intelligent contract deployed in a block chain and extracting a characteristic value of the first original data based on the first intelligent contract;

the matching module 203 is used for matching historical data stored in the block chain according to the characteristic value so as to determine whether target historical data with similarity to the first original data meeting a preset condition exists in the historical data;

and the processing module 204 performs corresponding processing according to the matching result information of the matching processing.

One or more embodiments of the present specification provide a data processing apparatus, which, when receiving a data processing request, extracts a feature value of first original data in the data processing request based on a first intelligent contract in a blockchain; and matching the historical data stored in the block chain according to the extracted characteristic value to determine whether the historical data has target historical data with the similarity meeting the preset condition with the first original data, and performing corresponding processing according to the matching result information of the matching processing. Therefore, by storing the work data of the user into the block chain and performing matching processing based on the historical data stored in the block chain when receiving the data processing request, the uncollapsibility of the data stored in the block chain is ensured, and the accuracy of the matching result is further ensured; and based on the characteristics of the block chain, the problems that the matching result is not credible and the like caused by being attacked are avoided. Meanwhile, by extracting the characteristic values and performing matching processing based on the characteristic values, the user is difficult to avoid even simply modifying the works, so that the accuracy of the matching result is improved; moreover, the characteristic value is extracted based on the intelligent contract, so that the extraction rule of the characteristic value can be traced, upgrading optimization can be performed, the extraction performance of the characteristic value is continuously improved, and the accuracy of the matching result is continuously improved.

Optionally, the extracting module 202 determines data type information of the first original data based on the first smart contract;

selecting a target second intelligent contract matched with the data type information from a plurality of second intelligent contracts deployed in advance, and extracting a characteristic value of the first original data based on the target second intelligent contract; and the second intelligent contract is provided with an extraction rule of the characteristic value of the first original data of the corresponding data type.

Optionally, the matching module 203 performs fuzzy search processing on the feature value of the first original data and the feature value of the historical data stored in the block chain, so as to obtain candidate historical data meeting a first preset condition from the historical data; and the number of the first and second groups,

calculating the similarity between the first original data and each candidate historical data according to a preset similarity calculation mode;

and determining whether target historical data with the similarity meeting a second preset condition with the first original data exists in the historical data according to the calculated similarity.

Optionally, the data processing request includes a data evidence storage request; the device also comprises a storage module;

the saving module is configured to, after the extracting module 202 extracts the feature value of the first original data based on the first smart contract, store the feature value and the first original data in association with each other in the block chain, and send a successful certificate saving message to the first user.

Optionally, if it is determined that the target historical data exists according to the matching result information of the matching process, the processing module 204 sends, to the first user, first prompt information that similar data exists according to the relevant information of the target historical data, and sends, to a second user corresponding to the target historical data, second prompt information that similar data exists according to the relevant information of the first original data.

Optionally, the receiving module 201 further receives the data certificate storing request sent by the first user; the data evidence storing request comprises second original data to be stored and obtained by updating the first original data based on the first prompt information; and carrying out evidence storage processing on the second original data.

Optionally, the data processing request comprises a retrieval request;

the processing module 204, if it is determined that the target history data exists according to the matching result information of the matching process, sends retrieval result information to the first user according to the related information of the target history data.

It should be noted that the embodiment of the data processing apparatus in this specification and the embodiment of the data processing method in this specification are based on the same inventive concept, and therefore, for specific implementation of this embodiment, reference may be made to implementation of the corresponding data processing method, and repeated details are not described again.

Further, corresponding to the data processing method described above, based on the same technical concept, one or more embodiments of the present specification further provide a data processing apparatus for executing the data processing method described above, and fig. 13 is a schematic structural diagram of the data processing apparatus provided in one or more embodiments of the present specification.

As shown in fig. 13, the data processing apparatus may have a relatively large difference due to different configurations or performances, and may include one or more processors 301 and a memory 302, and the memory 302 may store one or more stored applications or data. Memory 302 may be, among other things, transient storage or persistent storage. The application program stored in memory 302 may include one or more modules (not shown), each of which may include a series of computer-executable instructions in a data processing device. Still further, the processor 301 may be arranged in communication with the memory 302 to execute a series of computer executable instructions in the memory 302 on a data processing device. The data processing apparatus may also include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input-output interfaces 305, one or more keyboards 306, and the like.

In one particular embodiment, a data processing apparatus comprises a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may comprise one or more modules, and each module may comprise a series of computer-executable instructions for the data processing apparatus, and the one or more programs configured for execution by the one or more processors comprise computer-executable instructions for:

receiving a data processing request sent by a first user; the data processing request comprises first original data to be processed;

calling a first intelligent contract deployed in a block chain, and extracting a characteristic value of the first original data based on the first intelligent contract;

matching historical data stored in the block chain according to the characteristic value to determine whether target historical data with the similarity of the first original data meeting a preset condition exists in the historical data;

and carrying out corresponding processing according to the matching result information of the matching processing.

Optionally, the computer-executable instructions, when executed, extract feature values of the first raw data based on the first smart contract, comprising:

determining data type information of the first raw data based on the first smart contract;

Optionally, when executed, the computer-executable instructions perform matching processing on the historical data saved in the block chain according to the feature value to determine whether there is target historical data in the historical data, where similarity between the target historical data and the first original data meets a preset condition, where the matching processing includes:

fuzzy search processing is carried out on the characteristic value of the first original data and the characteristic value of historical data stored in the block chain, so that candidate historical data meeting a first preset condition are obtained from the historical data;

Optionally, the computer executable instructions, when executed, the data processing request comprises a data credentialing request;

after extracting the feature value of the first original data based on the first intelligent contract, the method further comprises:

and storing the characteristic value and the first original data in the block chain in an associated manner, and sending successful certificate storage information to the first user.

Optionally, the computer executable instructions, when executed, the data processing request comprises: a retrieval request;

the corresponding processing according to the matching result information of the matching processing comprises:

and if the target historical data is determined to exist according to the matching result information of the matching process, sending retrieval result information to the first user according to the related information of the target historical data.

Further, based on the same technical concept, one or more embodiments of the present specification further provide a storage medium for storing computer-executable instructions, where in a specific embodiment, the storage medium may be a usb disk, an optical disk, a hard disk, and the like, and when the storage medium stores the computer-executable instructions, the following processes can be implemented when the processor executes the computer-executable instructions:

One or more embodiments of the present specification provide a storage medium storing computer-executable instructions that, when executed by a processor, receive a data processing request sent by a first user, extract a feature value of first raw data in the data processing request based on a first intelligent contract in a blockchain; and matching the historical data stored in the block chain according to the extracted characteristic value to determine whether the historical data has target historical data with the similarity meeting the preset condition with the first original data, and performing corresponding processing according to the matching result information of the matching processing. Therefore, by storing the work data of the user into the block chain and performing matching processing based on the historical data stored in the block chain when receiving the data processing request, the uncollapsibility of the data stored in the block chain is ensured, and the accuracy of the matching result is further ensured; and based on the characteristics of the block chain, the problems that the matching result is not credible and the like caused by being attacked are avoided. Meanwhile, by extracting the characteristic values and performing matching processing based on the characteristic values, the user is difficult to avoid even simply modifying the works, so that the accuracy of the matching result is improved; moreover, the characteristic value is extracted based on the intelligent contract, so that the extraction rule of the characteristic value can be traced, upgrading optimization can be performed, the extraction performance of the characteristic value is continuously improved, and the accuracy of the matching result is continuously improved.

Optionally, the storage medium stores computer-executable instructions that, when executed by a processor, extract feature values of the first raw data based on the first smart contract, comprising:

Optionally, when executed by a processor, the computer-executable instructions stored in the storage medium perform matching processing on the historical data stored in the block chain according to the feature value to determine whether there is target historical data in the historical data, where similarity between the target historical data and the first original data meets a preset condition, where the matching processing includes:

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, the data processing request comprises a data credentialing request;

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, the data processing request comprises: a retrieval request;

It should be noted that the embodiment related to the storage medium in this specification and the embodiment related to the data processing method in this specification are based on the same inventive concept, and therefore, specific implementation of this embodiment may refer to implementation of the corresponding data processing method, and repeated details are not repeated.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by a first user programming the Device. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the units may be implemented in the same software and/or hardware or in multiple software and/or hardware when implementing the embodiments of the present description.

One skilled in the art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of this document and is not intended to limit this document. Various modifications and changes may occur to those skilled in the art from this document. Any modifications, equivalents, improvements, etc. which come within the spirit and principle of the disclosure are intended to be included within the scope of the claims of this document.

Claims

1. A data processing method is applied to a block chain node and comprises the following steps:

2. The method of claim 1, the extracting feature values of the first raw data based on the first smart contract, comprising:

3. The method of claim 2, the selecting a target second intelligent contract from a plurality of second intelligent contracts that are pre-deployed that matches the data type information, comprising:

acquiring associated target contract identification information in the association relationship between the preset data type information and the contract identification information according to the data type information;

and determining a second intelligent contract corresponding to the target contract identification information as a target second intelligent contract matched with the data type information.

4. The method of claim 1, the determining a characteristic value of the first raw data based on the first smart contract, comprising:

acquiring a target extraction rule in the incidence relation between the data type information and the extraction rule included in the first intelligent contract according to the data type information;

and extracting the characteristic value of the first original data according to the target extraction rule.

5. The method of claim 2 or 4, the determining data type information for the first raw data based on the first smart contract, comprising:

based on the first intelligent contract, identifying the first original data according to a preset type identification mode to obtain data type information of the first original data; alternatively, the first and second electrodes may be,

and acquiring the data type information of the first original data from the data processing request based on the first intelligent contract.

6. The method according to claim 1, wherein the matching, according to the feature value, the historical data saved in the block chain to determine whether there is target historical data in the historical data, of which the similarity with the first original data meets a preset condition, includes:

7. The method according to claim 6, wherein the performing a fuzzy search process on the feature value of the first raw data and the feature value of the historical data saved in the blockchain to obtain candidate historical data meeting a first preset condition from the historical data includes:

according to a preset indexing mode, carrying out indexing processing on the characteristic value of the first original data and the characteristic value corresponding to the historical data stored in the block chain to obtain a target characteristic value meeting a first preset condition;

and determining the historical data corresponding to the target characteristic value as the candidate historical data.

8. The method of claim 1, the data processing request comprising a data credentialing request;

9. The method according to claim 8, wherein the corresponding processing is performed according to the matching result information of the matching processing, and comprises:

if the target historical data is determined to exist according to the matching result information of the matching process, sending first prompt information with similar data to the first user according to the relevant information of the target historical data, and sending second prompt information with similar data to a second user corresponding to the target historical data according to the relevant information of the first original data.

10. The method of claim 9, after sending a first prompt to the first user for similar data according to the information related to the target historical data, further comprising:

receiving the data evidence storing request sent by the first user; the data evidence storing request comprises second original data to be stored and obtained by updating the first original data based on the first prompt information;

and carrying out evidence storage processing on the second original data.

11. The method of claim 1, the data processing request comprising: a retrieval request;

12. A data processing device applied to a blockchain node comprises:

the receiving module is used for receiving a data processing request sent by a first user; the data processing request comprises first original data to be processed;

the extraction module is used for calling a first intelligent contract deployed in a block chain and extracting a characteristic value of the first original data based on the first intelligent contract;

the matching module is used for matching historical data stored in the block chain according to the characteristic value so as to determine whether target historical data with the similarity meeting a preset condition with the first original data exists in the historical data;

and the processing module is used for carrying out corresponding processing according to the matching result information of the matching processing.

13. The apparatus as set forth in claim 12, wherein,

the extraction module is used for determining data type information of the first original data based on the first intelligent contract;

14. The apparatus as set forth in claim 12, wherein,

the matching module is used for carrying out fuzzy search processing on the characteristic value of the first original data and the characteristic value of the historical data stored in the block chain so as to acquire candidate historical data meeting a first preset condition from the historical data;

15. The apparatus of claim 12, the data processing request comprising a data credentialing request; the device also comprises a storage module;

the storage module is used for storing the characteristic value and the first original data into the block chain in an associated manner after the extraction module extracts the characteristic value of the first original data based on the first intelligent contract, and sending successful evidence storage information to the first user.

16. The apparatus of claim 12, the data processing request comprising a retrieval request;

and the processing module is used for sending retrieval result information to the first user according to the relevant information of the target historical data if the target historical data is determined to exist according to the matching result information of the matching processing.

17. A data processing apparatus comprising:

a processor; and the number of the first and second groups,

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

18. A storage medium storing computer-executable instructions that when executed by a processor implement the following: